AI-assisted Avenues for Linguistic Assessment and Intervention Methods in Speech-Language Pathology

Graduate students in Speech-Language Pathology learn, refine, and utilize linguistic interventions to assist in the clinical remediation or management of language disorders. Large Language Models (LLMs) and associated multimedia AI platforms offer rich opportunities for graduate clinicians to integrate this technology into responsive intervention methods that help monitor and adjust for real-time fluctuations in client comprehension and expressive needs. Clinical educators often use clinical artifacts and problem-solving to contextualize learning for future clinicians in the classroom, but how can Artificial Intelligence augment this experience while optimizing for agency and integrity? One main objective of this session is to demonstrate how AI platforms can be capitalized to augment the translational nature of graduate students’ clinical training. This demonstration session details modeling and instruction across clinical artifacts pertinent to AI-assisted, responsive, linguistic intervention methods in the clinical context: receptive/expressive vocabulary demands, grammaticality judgment tasks, narrative scaffolds/story elements, literacy-based activity scaffolds, and pragmatic language (social-cognitive) targets. These clinical scenarios also highlight the intersection of linguistic interventions and response generation using associated AI products. Conference attendees will engage in intriguing discussions on the ramifications of utilizing the AI in the context of social-behavioral clinical training. Participants can expect to acquire a deeper understanding of the opportunities and intersections associated with AI technology and clinical education. Given that linguistic skill development is relevant across the curriculum at every layer of discourse complexity, the AI-assisted language activities in this demonstration session can be adapted to augment the study skills of higher education students in any field.